Public Member Functions | Protected Member Functions | Protected Attributes

CPerceptron Class Reference

Detailed Description

Class Perceptron implements the standard linear (online) perceptron.

Given a maximum number of iterations (the standard perceptron algorithm is not guaranteed to converge) and a fixed lerning rate, the result is a linear classifier.

See also:

Definition at line 30 of file Perceptron.h.

Inheritance diagram for CPerceptron:
Inheritance graph

List of all members.

Public Member Functions

 CPerceptron ()
 CPerceptron (CDotFeatures *traindat, CLabels *trainlab)
virtual ~CPerceptron ()
virtual EClassifierType get_classifier_type ()
void set_learn_rate (float64_t r)
 set learn rate of gradient descent training algorithm
void set_max_iter (int32_t i)
 set maximum number of iterations
virtual const char * get_name () const

Protected Member Functions

virtual bool train_machine (CFeatures *data=NULL)

Protected Attributes

float64_t learn_rate
int32_t max_iter

Constructor & Destructor Documentation

CPerceptron (  ) 

default constructor

Definition at line 17 of file Perceptron.cpp.

CPerceptron ( CDotFeatures traindat,
CLabels trainlab 


traindat training features
trainlab labels for training features

Definition at line 22 of file Perceptron.cpp.

~CPerceptron (  )  [virtual]

Definition at line 29 of file Perceptron.cpp.

Member Function Documentation

virtual EClassifierType get_classifier_type (  )  [virtual]

get classifier type

classifier type PERCEPTRON

Reimplemented from CMachine.

Definition at line 48 of file Perceptron.h.

virtual const char* get_name (  )  const [virtual]
object name

Reimplemented from CLinearMachine.

Definition at line 63 of file Perceptron.h.

void set_learn_rate ( float64_t  r  ) 

set learn rate of gradient descent training algorithm

Definition at line 51 of file Perceptron.h.

void set_max_iter ( int32_t  i  ) 

set maximum number of iterations

Definition at line 57 of file Perceptron.h.

bool train_machine ( CFeatures data = NULL  )  [protected, virtual]

train classifier

data training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data)
whether training was successful

Reimplemented from CMachine.

Definition at line 33 of file Perceptron.cpp.

Member Data Documentation

float64_t learn_rate [protected]

learning rate

Definition at line 78 of file Perceptron.h.

int32_t max_iter [protected]

maximum number of iterations

Definition at line 80 of file Perceptron.h.

The documentation for this class was generated from the following files:
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SHOGUN Machine Learning Toolbox - Documentation